ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Role of ivermectin in the prevention of SARS-CoV-2 infection among healthcare workers in India: A matched case-control study
This article has 13 authors:Reviewed by ScreenIT
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Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases
This article has 7 authors:Reviewed by ScreenIT
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Rapid and accurate agglutination-based testing for SARS-CoV-2 antibodies
This article has 17 authors:Reviewed by ScreenIT
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A simplified approach to monitoring the COVID-19 epidemiologic situation using waste water analysis and its application in Russia
This article has 5 authors:Reviewed by ScreenIT
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Genetically predicted serum vitamin D and COVID-19: a Mendelian randomisation study
This article has 5 authors:Reviewed by ScreenIT
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Nowcasting the COVID‐19 pandemic in Bavaria
This article has 5 authors:Reviewed by ScreenIT
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Emergence of novel SARS-CoV-2 variants in the Netherlands
This article has 2 authors:Reviewed by ScreenIT
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Assessing the performance of a serological point-of-care test in measuring detectable antibodies against SARS-CoV-2
This article has 24 authors:Reviewed by ScreenIT
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Clinical Mortality in a Large COVID-19 Cohort: Observational Study
This article has 7 authors:Reviewed by ScreenIT
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Excessive Matrix Metalloproteinase-1 and Hyperactivation of Endothelial Cells Occurred in COVID-19 Patients and Were Associated With the Severity of COVID-19
This article has 7 authors:Reviewed by ScreenIT